scholarly journals Distributed Two-Dimensional MUSIC for Joint Range and Angle Estimation with Distributed FMCW MIMO Radars

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7618
Author(s):  
Jiho Seo ◽  
Jonghyeok Lee ◽  
Jaehyun Park ◽  
Hyungju Kim ◽  
Sungjin You
Keyword(s):  
Data Fusion ◽  
Cost Function ◽  
Data Communication ◽  
Region Of Interest ◽  
Estimation Accuracy ◽  
Communication Overhead ◽  
Fusion Center ◽  
Music Algorithm ◽  
Image Region ◽  
Joint Range

To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.

scholarly journals An Efficient Contention-Window Based Reporting for Internet of Things Features in Cognitive Radio Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Junsu Kim ◽  
Eung Hyuk Lee ◽  
Su Min Kim
Keyword(s):  
Cognitive Radio ◽  
Internet Of Things ◽  
Evidence Theory ◽  
Radio Networks ◽  
Contention Window ◽  
Communication Overhead ◽  
Fusion Center ◽  
Bandwidth Utilization ◽  
Secondary Users ◽  
Reporting Scheme

Internet of things (IoT) is a new challenging paradigm for connecting heterogeneous networks. However, an explosive increase in the number of IoT cognitive users requires a mass of sensing reporting; thus, it increases complexity of the system. Moreover, bandwidth utilization, reporting time, and communication overhead arise. To realize spectrum sensing, how to collect sensing results by reducing the communication overhead and the reporting time is a problem of major concern in future wireless networks. On the other hand, cognitive radio is a promising technology to access the spectrum opportunistically. In this paper, we propose a contention-window based reporting approach with a sequential fusion mechanism. The proposed reporting scheme reduces the reporting time and the communication overhead by collecting sensing results from the secondary users with the highest reliability at a fusion center by utilizing Dempster-Shafer evidence theory. The fusion center broadcasts the sensing results once a global decision requirement is satisfied. Through simulations, we evaluate the proposed scheme in terms of percentage of the number of reporting secondary users, error probability, percentage of reporting, and spectral efficiency. As a result, it is shown that the proposed scheme is more effective than a conventional order-less sequential reporting scheme.

scholarly journals Pseudo-Random Encryption for Security Data Transmission in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2452 ◽  
Author(s):  
Liang Liu ◽  
Wen Chen ◽  
Tao Li ◽  
Yuling Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Transmission ◽  
Multiple Scales ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Inverse Mapping ◽  
The Matrix

The security of wireless sensor networks (WSN) has become a great challenge due to the transmission of sensor data through an open and wireless network with limited resources. In the paper, we discussed a lightweight security scheme to protect the confidentiality of data transmission between sensors and an ally fusion center (AFC) over insecure links. For the typical security problem of WSN’s binary hypothesis testing of a target’s state, sensors were divided into flipping and non-flipping groups according to the outputs of a pseudo-random function which was held by sensors and the AFC. Then in order to prevent an enemy fusion center (EFC) from eavesdropping, the binary outputs from the flipping group were intentionally flipped to hinder the EFC’s data fusion. Accordingly, the AFC performed inverse flipping to recover the flipped data before data fusion. We extended the scheme to a more common scenario with multiple scales of sensor quantification and candidate states. The underlying idea was that the sensor measurements were randomly mapped to other quantification scales using a mapping matrix, which ensured that as long as the EFC was not aware of the matrix, it could not distract any useful information from the captured data, while the AFC could appropriately perform data fusion based on the inverse mapping of the sensor outputs.

scholarly journals An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 274 ◽  
Author(s):  
Shengying Yang ◽  
Huibin Qin ◽  
Xiaolin Liang ◽  
Thomas Gulliver
Keyword(s):  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Fingerprint Analysis ◽  
Detection Algorithms ◽  
Rf Signals ◽  
Aerial Vehicle ◽  
Value Decomposition

Unmanned aerial vehicles (UAVs) are now readily available worldwide and users can easily fly them remotely using smart controllers. This has created the problem of keeping unauthorized UAVs away from private or sensitive areas where they can be a personal or public threat. This paper proposes an improved radio frequency (RF)-based method to detect UAVs. The clutter (interference) is eliminated using a background filtering method. Then singular value decomposition (SVD) and average filtering are used to reduce the noise and improve the signal to noise ratio (SNR). Spectrum accumulation (SA) and statistical fingerprint analysis (SFA) are employed to provide two frequency estimates. These estimates are used to determine if a UAV is present in the detection environment. The data size is reduced using a region of interest (ROI), and this improves the system efficiency and improves azimuth estimation accuracy. Detection results are obtained using real UAV RF signals obtained experimentally which show that the proposed method is more effective than other well-known detection algorithms. The recognition rate with this method is close to 100% within a distance of 2.4 km and greater than 90% within a distance of 3 km. Further, multiple UAVs can be detected accurately using the proposed method.

Intelligent Computer Aided Detection of Tumor in MRI Brain Images Using Cascaded Correlation Neural Network Classifier

2014 ◽  
Vol 626 ◽  
pp. 65-71
Author(s):  
V. Amsaveni ◽  
N. Albert Singh ◽  
J. Dheeba
Keyword(s):  
Neural Network ◽  
Region Of Interest ◽  
Texture Features ◽  
Neural Network Classifier ◽  
Image Region ◽  
Brain Images ◽  
Gabor Features ◽  
Computer Aided ◽  
Pixel Location ◽  
Nonlinear Classifier

In this paper, a Computer aided classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI is proposed. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). The extracted Gabor features from MRI is given as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Based on the analysis the performance of the proposed cascaded correlation neural network classifier is superior when compared with other classification approaches.

The Data Fusion for MIMO Radar

2010 ◽  
Vol 121-122 ◽  
pp. 627-632 ◽  
Author(s):  
Jian Kui Zeng ◽  
Zi Ming Dong
Keyword(s):  
Data Fusion ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Fusion Center ◽  
Fusion Technique ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Radar Technique

Multiple Input Multiple Output (MIMO) radar is a new emerging radar technique developed recently. In this paper, the principle of MIMO Radar based on transmitting diversity is described and then the data fusion technique for MIMO radar is presented. In this method, the detection result of each detector of MIMO radar is integrated in data fusion center, a final detection result is get which includes all the information of each detector result.

scholarly journals Adaptive Parameter Estimation of IIR System-Based WSN Using Multihop Diffusion in Distributed Approach

2020 ◽  
Vol 14 (4) ◽  
pp. 30-41
Author(s):  
Meera Dash ◽  
Trilochan Panigrahi ◽  
Renu Sharma ◽  
Mihir Narayan Mohanty
Keyword(s):  
Impulse Response ◽  
Sensor Node ◽  
Finite Impulse Response ◽  
Lms Algorithm ◽  
Sensor System ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Infinite Impulse Response ◽  
Communication Overhead ◽  
Least Mean Square

Distributed estimation of parameters in wireless sensor networks is taken into consideration to reduce the communication overhead of the network which makes the sensor system energy efficient. Most of the distributed approaches in literature, the sensor system is modeled with finite impulse response as it is inherently stable. Whereas in real time applications of WSN like target tracking, fast rerouting requires, infinite impulse response system (IIR) is used to model and that has been chosen in this work. It is assumed that every sensor node is equipped with IIR adaptive system. The diffusion least mean square (DLMS) algorithm is used to estimate the parameters of the IIR system where each node in the network cooperates themselves. In a sparse WSN, the performance of a DLMS algorithm reduces as the degree of the node decreases. In order to increase the estimation accuracy with a smaller number of iterations, the sensor node needs to share their information with more neighbors. This is feasible by communicating each node with multi-hop nodes instead of one-hop only. Therefore the parameters of an IIR system is estimated in distributed sparse sensor network using multihop diffusion LMS algorithm. The simulation results exhibit superior performance of the multihop diffusion LMS over non-cooperative and conventional diffusion algorithms.

scholarly journals Fusing Administrative Data to Combat the Opioid Crisis

2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Kevin P Conway ◽  
Camille Gourdet
Keyword(s):  
Data Fusion ◽  
Administrative Data ◽  
Addiction Treatment ◽  
Criminal Liability ◽  
Legal Protection ◽  
The United States ◽  
Opioid Antagonist ◽  
Fusion Center ◽  
Opioid Crisis ◽  
Overdose Deaths

Opioid-related overdose deaths remain the leading cause of unintentional injury fatalities in the United States.  State lawmakers have responded to this crisis by establishing a regulatory environment that extends various legal protections to persons who may help save the life of someone experiencing an opioid-related overdose.  Most states now protect specific parties (e.g., doctors, pharmacists, first responders, laypersons) from civil or criminal liability who prescribe, dispense, possess or administer an opioid antagonist in accordance with the provisions of the state’s law.  In addition to standing orders that facilitate access to opioid antagonists, many states offer legal protection to “Good Samaritans” seeking medical and emergency assistance for a person experiencing an overdose.  Some states additionally mandate that addiction-treatment services be offered in conjunction with the dispensing of an opioid antagonist, whereas others designate revenue to purchase opiate antagonists or to fund treatment programs. Little is known about the potential impact of such regulatory actions on the opioid crisis.  RTI’s Data Fusion Center seeks to meet this need by combining administrative data across sources and systems to inform research and policy.  The current paper describes the Data Fusion Center and presents preliminary results from a study that predicts opioid-related overdose deaths based on the existence and strength of opioid-related state laws among 50 states from 2006 to 2016.  Policy data were webscraped from state agencies, systematically coded, and associated with target outcomes sourced from CDC.  Study findings may help inform lawmakers and stakeholders in prioritizing data-driven policy responses to the opioid crisis.

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